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誰もが私たちの人生の貴重を認識する必要があります。時間を無駄にすることはできないので、目標をまっすぐに達成するための良い方法が必要です。もちろん、最新のAB-731試験トレントが最適です。 AB-731試験の質問から、認定試験の知識だけでなく、質問に迅速かつ正確に回答する方法を学ぶことができることをお約束します。今、AB-731テストトレントのデモを無料でダウンロードして、すばらしい品質を確認できます。
質問 # 67
Your company purchases Microsoft 365 Copilot for its sales department. The sales department needs to find and summarize information across internal documents quickly. From which two data sources can the sales department obtain results by default? (Select TWO.)
正解：C、D
質問 # 68
In which scenario is Azure Machine Learning most likely to deliver strategic value for an organization?
正解：A
解説：
Azure Machine Learning (Azure ML) delivers strategic value by transforming historical sales data into a competitive advantage through advanced demand forecasting. By identifying complex patterns in past consumer behavior, it helps businesses optimize high-stakes operational areas such as inventory management, production planning, and resource allocation.
Benefits
Inventory Optimization: Businesses can maintain leaner inventory levels, drastically reducing storage costs and minimizing the risk of both stockouts and overstocking.
Financial Performance: Improved forecast accuracy directly protects margins by reducing the need for emergency shipping, overtime labor, and waste from unsold goods.
Strategic Growth: Accurate long-term forecasts provide a reliable roadmap for planning product launches, marketing promotions, and market expansion.
Operational Agility: Azure ML's Automated Machine Learning (AutoML) allows companies to quickly adapt to market shifts-like seasonal trends or unexpected disruptions-by continuously learning from new data.
Reference:
https://learn.microsoft.com/en-us/azure/architecture/ai-ml/idea/next-order-forecasting
質問 # 69
During AI adoption planning, leadership evaluates workforce readiness, operating models, and governance structures required to support AI at scale. Why is this step critical?
正解：B
解説：
Assessing workforce skills, governance, and operating models ensures the organization can adopt AI responsibly and scale usage effectively.
Reference:
https://learn.microsoft.com/en-us/azure/cloud-adoption-framework/scenarios/ai/plan
質問 # 70
For each of the following statements, select Yes if the statement is true. Otherwise, select No. NOTE: Each correct selection is worth one point.
正解：
解説：
Explanation:
* A manufacturer can use Azure Vision in Foundry Tools to identify product defects on an assembly line.
The answer: Yes
* A logistics company can use Azure Vision in Foundry Tools to recognize package shipping labels.
The answer: Yes
* The HR department at your company can only use Azure Vision in Foundry Tools to extract written content from Microsoft Word files. Answer: No Azure Vision in Foundry Tools provides computer vision capabilities to analyze images, including identifying visual features and reading text with OCR. Because it is designed to "analyze images" and support vision scenarios, it can be applied to manufacturing quality inspection use cases where the goal is to detect anomalies/defects from images captured on a production line. This aligns with statement 1 being Yes .
Statement 2 is also Yes because recognizing shipping labels is fundamentally text extraction from images (often plus some layout/field parsing). Azure Vision supports optical character recognition (OCR) to read printed text from images, and Microsoft documentation explicitly notes OCR can extract text from images such as product labels and similar real-world text surfaces-making shipping labels a direct fit.
Statement 3 is No because it is incorrectly restrictive. Azure Vision is not limited to extracting written content from Word documents, nor is OCR restricted to Word files. Vision capabilities apply broadly to images (and, depending on the capability, various document/image inputs) for tasks like image analysis and text recognition. HR could use it for many scenarios such as extracting text from scanned images, photos, or other visual inputs-not "only" Word files.
質問 # 71
Hotspot Question
Select the answer that correctly completes the sentence.
正解：
解説：
Explanation:
Box: model inaccuracy
When a generative AI model produces output that seems realistic but contains incorrect information, the behavior is known as _______________.
That specific behavior-where the AI generates plausible-sounding but factually incorrect information-is known as hallucination.
While "model inaccuracy" is a broad way to describe it, "hallucination" specifically refers to when a generative AI model-like a large language model (LLM)-produces incorrect, misleading, or entirely fabricated information while presenting it as fact with a confident and plausible tone.
Reference:
https://www.techtimes.com/articles/314230/20260122/ai-hallucinations-explained-why-generative- ai-often-produces-inaccurate-results.htm
質問 # 72
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